Where does the data live?
Sorgente, persistenza, retention e perimetro.
Private AI & Local Inference
Not all data can leave. Not all models need to live outside.
I design AI architectures where models, data, and applications are placed in the right environment — public cloud, private cloud, on-premise, or local — based on risk, budget, performance, and governance.
AI accelerates. Systems thinking governs.
Where the model runs is a data governance choice.
Private AI doesn't mean demonizing public LLMs. It means deliberately deciding where to place data, models, and logs based on risk and context.
When it's needed
- Dati riservati
- Documenti interni
- Contratti
- Dati sanitari o sensibili
- Codice sorgente
- Procedure operative
- Dati pubblica amministrazione
- Intellectual property
- Vincoli normativi
Opzioni architetturali
- LLM cloud pubblici
- API esterne controllate
- Modelli open source self-hosted
- Inferenza locale su GPU
- Server dedicati
- Cluster containerizzati
- RAG privato
- Ambienti air-gapped o semi-isolati
Domande corrette
Architecture comes before tools. Technical placement must answer verifiable questions.
Where does the model run?
Cloud, privato, on-premise, GPU locale o ibrido.
What gets logged?
Prompt, output, embedding, metadati e accessi.
Who can access it?
Ruoli, policy, audit e isolamento.
What does inference cost?
GPU, API, latenza, throughput e manutenzione.
Quali output vanno verificati?
Technical gates before automations or operational decisions.
Output
Artefatti concreti per rendere controllabile un sistema IA.
- Architecture IA privata
- Proof of concept
- RAG interno
- Sistema documentale conversazionale
- Local LLM infrastructure
- Deploy containerizzato
- Access policies
- Technical documentation
Evaluate a private AI solution
Dati, modelli e applicazioni devono stare nel posto giusto. Partiamo dal rischio e dal contesto.